Overview

Dataset statistics

Number of variables17
Number of observations6966
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory640.7 KiB
Average record size in memory94.2 B

Variable types

Categorical5
Numeric9
Boolean3

Alerts

rental_date has a high cardinality: 364 distinct values High cardinality
rental_month is highly correlated with rental_yearHigh correlation
rental_year is highly correlated with rental_monthHigh correlation
rental_month is highly correlated with rental_yearHigh correlation
rental_year is highly correlated with rental_monthHigh correlation
rental_hour is highly correlated with rental_year and 1 other fieldsHigh correlation
rental_day is highly correlated with rental_year and 1 other fieldsHigh correlation
rental_month is highly correlated with rental_year and 1 other fieldsHigh correlation
rental_year is highly correlated with rental_hour and 3 other fieldsHigh correlation
dayofweek_n is highly correlated with rainHigh correlation
rain is highly correlated with rental_hour and 4 other fieldsHigh correlation
working_day is highly correlated with dayofweekHigh correlation
dayofweek is highly correlated with working_dayHigh correlation
rental_year is highly correlated with seasonHigh correlation
season is highly correlated with rental_yearHigh correlation
rental_hour is highly correlated with peak and 1 other fieldsHigh correlation
rental_month is highly correlated with rental_year and 2 other fieldsHigh correlation
rental_year is highly correlated with rental_month and 2 other fieldsHigh correlation
dayofweek_n is highly correlated with dayofweek and 1 other fieldsHigh correlation
dayofweek is highly correlated with dayofweek_n and 1 other fieldsHigh correlation
working_day is highly correlated with dayofweek_n and 2 other fieldsHigh correlation
season is highly correlated with rental_month and 2 other fieldsHigh correlation
peak is highly correlated with rental_hour and 1 other fieldsHigh correlation
timesofday is highly correlated with rental_hourHigh correlation
temp is highly correlated with rental_month and 2 other fieldsHigh correlation
rental_date is uniformly distributed Uniform
rental_hour has 266 (3.8%) zeros Zeros
dayofweek_n has 962 (13.8%) zeros Zeros
rain has 6324 (90.8%) zeros Zeros

Reproduction

Analysis started2022-04-10 12:37:29.218385
Analysis finished2022-04-10 12:38:02.054589
Duration32.84 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

rental_date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct364
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size54.5 KiB
2021-12-15
 
24
2021-07-24
 
24
2022-02-05
 
24
2022-02-04
 
24
2021-06-27
 
23
Other values (359)
6847 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-03-01
2nd row2021-03-01
3rd row2021-03-01
4th row2021-03-01
5th row2021-03-01

Common Values

ValueCountFrequency (%)
2021-12-1524
 
0.3%
2021-07-2424
 
0.3%
2022-02-0524
 
0.3%
2022-02-0424
 
0.3%
2021-06-2723
 
0.3%
2021-09-0423
 
0.3%
2021-08-0123
 
0.3%
2022-02-1623
 
0.3%
2022-02-1123
 
0.3%
2021-09-1123
 
0.3%
Other values (354)6732
96.6%

Length

2022-04-10T13:38:02.300432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-12-1524
 
0.3%
2022-02-0424
 
0.3%
2021-07-2424
 
0.3%
2022-02-0524
 
0.3%
2021-09-0423
 
0.3%
2021-08-0123
 
0.3%
2022-02-1623
 
0.3%
2022-02-1123
 
0.3%
2021-09-1123
 
0.3%
2022-01-2723
 
0.3%
Other values (354)6732
96.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rental_hour
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.82931381
Minimum0
Maximum23
Zeros266
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2022-04-10T13:38:02.501074image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median13
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.3472844
Coefficient of variation (CV)0.4947485496
Kurtosis-0.8357483945
Mean12.82931381
Median Absolute Deviation (MAD)5
Skewness-0.2775842676
Sum89369
Variance40.28801926
MonotonicityNot monotonic
2022-04-10T13:38:02.729172image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
17360
 
5.2%
18358
 
5.1%
14358
 
5.1%
13356
 
5.1%
11355
 
5.1%
15354
 
5.1%
16352
 
5.1%
12352
 
5.1%
9352
 
5.1%
10349
 
5.0%
Other values (14)3420
49.1%
ValueCountFrequency (%)
0266
3.8%
1168
2.4%
2149
2.1%
3130
 
1.9%
4117
 
1.7%
5134
 
1.9%
6222
3.2%
7293
4.2%
8348
5.0%
9352
5.1%
ValueCountFrequency (%)
23278
4.0%
22306
4.4%
21317
4.6%
20344
4.9%
19348
5.0%
18358
5.1%
17360
5.2%
16352
5.1%
15354
5.1%
14358
5.1%

rental_day
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.64053976
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2022-04-10T13:38:02.944728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.689671829
Coefficient of variation (CV)0.5555864414
Kurtosis-1.177659354
Mean15.64053976
Median Absolute Deviation (MAD)7
Skewness0.004253637489
Sum108952
Variance75.51039649
MonotonicityNot monotonic
2022-04-10T13:38:03.210286image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
24240
 
3.4%
18239
 
3.4%
11236
 
3.4%
22236
 
3.4%
12236
 
3.4%
13235
 
3.4%
23235
 
3.4%
15235
 
3.4%
17234
 
3.4%
16234
 
3.4%
Other values (21)4606
66.1%
ValueCountFrequency (%)
1224
3.2%
2225
3.2%
3225
3.2%
4232
3.3%
5231
3.3%
6225
3.2%
7234
3.4%
8225
3.2%
9232
3.3%
10222
3.2%
ValueCountFrequency (%)
31106
1.5%
30192
2.8%
29210
3.0%
28209
3.0%
27233
3.3%
26230
3.3%
25227
3.3%
24240
3.4%
23235
3.4%
22236
3.4%

rental_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.557565317
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2022-04-10T13:38:03.426467image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.437607482
Coefficient of variation (CV)0.5242200901
Kurtosis-1.196971805
Mean6.557565317
Median Absolute Deviation (MAD)3
Skewness-0.04555345272
Sum45680
Variance11.8171452
MonotonicityNot monotonic
2022-04-10T13:38:03.624110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8618
8.9%
10617
8.9%
7600
8.6%
6599
8.6%
1599
8.6%
9585
8.4%
11583
8.4%
5566
8.1%
12564
8.1%
3555
8.0%
Other values (2)1080
15.5%
ValueCountFrequency (%)
1599
8.6%
2544
7.8%
3555
8.0%
4536
7.7%
5566
8.1%
6599
8.6%
7600
8.6%
8618
8.9%
9585
8.4%
10617
8.9%
ValueCountFrequency (%)
12564
8.1%
11583
8.4%
10617
8.9%
9585
8.4%
8618
8.9%
7600
8.6%
6599
8.6%
5566
8.1%
4536
7.7%
3555
8.0%

rental_year
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.5 KiB
2021
5823 
2022
1143 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
20215823
83.6%
20221143
 
16.4%

Length

2022-04-10T13:38:03.822287image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-10T13:38:03.947189image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
20215823
83.6%
20221143
 
16.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

holiday
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
6684 
True
 
282
ValueCountFrequency (%)
False6684
96.0%
True282
 
4.0%
2022-04-10T13:38:04.031918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

dayofweek_n
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.031007752
Minimum0
Maximum6
Zeros962
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2022-04-10T13:38:04.168923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.989971351
Coefficient of variation (CV)0.6565378625
Kurtosis-1.241328444
Mean3.031007752
Median Absolute Deviation (MAD)2
Skewness-0.02522150698
Sum21114
Variance3.959985976
MonotonicityNot monotonic
2022-04-10T13:38:04.386758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
51031
14.8%
41021
14.7%
2997
14.3%
3993
14.3%
6988
14.2%
1974
14.0%
0962
13.8%
ValueCountFrequency (%)
0962
13.8%
1974
14.0%
2997
14.3%
3993
14.3%
41021
14.7%
51031
14.8%
6988
14.2%
ValueCountFrequency (%)
6988
14.2%
51031
14.8%
41021
14.7%
3993
14.3%
2997
14.3%
1974
14.0%
0962
13.8%

dayofweek
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
Saturday
1031 
Friday
1021 
Wednesday
997 
Thursday
993 
Sunday
988 
Other values (2)
1936 

Length

Max length9
Median length7
Mean length7.150301464
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMonday
2nd rowMonday
3rd rowMonday
4th rowMonday
5th rowMonday

Common Values

ValueCountFrequency (%)
Saturday1031
14.8%
Friday1021
14.7%
Wednesday997
14.3%
Thursday993
14.3%
Sunday988
14.2%
Tuesday974
14.0%
Monday962
13.8%

Length

2022-04-10T13:38:04.616753image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-10T13:38:04.794801image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
saturday1031
14.8%
friday1021
14.7%
wednesday997
14.3%
thursday993
14.3%
sunday988
14.2%
tuesday974
14.0%
monday962
13.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

working_day
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
4722 
False
2244 
ValueCountFrequency (%)
True4722
67.8%
False2244
32.2%
2022-04-10T13:38:04.906335image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

season
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Summer
1847 
Autumn
1741 
Spring
1704 
Winter
1674 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWinter
2nd rowWinter
3rd rowWinter
4th rowWinter
5th rowWinter

Common Values

ValueCountFrequency (%)
Summer1847
26.5%
Autumn1741
25.0%
Spring1704
24.5%
Winter1674
24.0%

Length

2022-04-10T13:38:05.047853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-10T13:38:05.233756image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
summer1847
26.5%
autumn1741
25.0%
spring1704
24.5%
winter1674
24.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

peak
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4634 
True
2332 
ValueCountFrequency (%)
False4634
66.5%
True2332
33.5%
2022-04-10T13:38:05.324151image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

timesofday
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Afternoon
2132 
Morning
1697 
Evening
1673 
Night
1464 

Length

Max length9
Median length7
Mean length7.191788688
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight
2nd rowMorning
3rd rowMorning
4th rowMorning
5th rowMorning

Common Values

ValueCountFrequency (%)
Afternoon2132
30.6%
Morning1697
24.4%
Evening1673
24.0%
Night1464
21.0%

Length

2022-04-10T13:38:05.446274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-10T13:38:05.602813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
afternoon2132
30.6%
morning1697
24.4%
evening1673
24.0%
night1464
21.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rain
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct44
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05961814528
Minimum0
Maximum10.3
Zeros6324
Zeros (%)90.8%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2022-04-10T13:38:05.767895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.3
Maximum10.3
Range10.3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3295040944
Coefficient of variation (CV)5.526909515
Kurtosis209.3934453
Mean0.05961814528
Median Absolute Deviation (MAD)0
Skewness11.40675912
Sum415.3
Variance0.1085729482
MonotonicityNot monotonic
2022-04-10T13:38:06.009977image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
06324
90.8%
0.1195
 
2.8%
0.286
 
1.2%
0.356
 
0.8%
0.444
 
0.6%
0.638
 
0.5%
0.528
 
0.4%
0.726
 
0.4%
0.821
 
0.3%
0.919
 
0.3%
Other values (34)129
 
1.9%
ValueCountFrequency (%)
06324
90.8%
0.1195
 
2.8%
0.286
 
1.2%
0.356
 
0.8%
0.444
 
0.6%
0.528
 
0.4%
0.638
 
0.5%
0.726
 
0.4%
0.821
 
0.3%
0.919
 
0.3%
ValueCountFrequency (%)
10.31
< 0.1%
5.51
< 0.1%
5.21
< 0.1%
5.11
< 0.1%
4.91
< 0.1%
4.71
< 0.1%
4.61
< 0.1%
4.51
< 0.1%
4.21
< 0.1%
3.61
< 0.1%

temp
Real number (ℝ)

HIGH CORRELATION

Distinct284
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.74239162
Minimum-4
Maximum26.3
Zeros7
Zeros (%)0.1%
Negative54
Negative (%)0.8%
Memory size54.5 KiB
2022-04-10T13:38:06.258019image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile2.6
Q17.025
median10.6
Q314.5
95-th percentile18.775
Maximum26.3
Range30.3
Interquartile range (IQR)7.475

Descriptive statistics

Standard deviation5.002159358
Coefficient of variation (CV)0.4656467141
Kurtosis-0.4056822749
Mean10.74239162
Median Absolute Deviation (MAD)3.7
Skewness0.09826056311
Sum74831.5
Variance25.02159824
MonotonicityNot monotonic
2022-04-10T13:38:06.512341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.169
 
1.0%
866
 
0.9%
10.764
 
0.9%
10.664
 
0.9%
13.264
 
0.9%
7.663
 
0.9%
8.963
 
0.9%
8.762
 
0.9%
9.362
 
0.9%
11.360
 
0.9%
Other values (274)6329
90.9%
ValueCountFrequency (%)
-41
 
< 0.1%
-3.41
 
< 0.1%
-3.31
 
< 0.1%
-2.93
< 0.1%
-2.81
 
< 0.1%
-2.61
 
< 0.1%
-2.51
 
< 0.1%
-2.31
 
< 0.1%
-2.11
 
< 0.1%
-22
< 0.1%
ValueCountFrequency (%)
26.33
< 0.1%
26.21
 
< 0.1%
25.91
 
< 0.1%
25.72
< 0.1%
25.61
 
< 0.1%
25.43
< 0.1%
25.32
< 0.1%
25.21
 
< 0.1%
25.12
< 0.1%
251
 
< 0.1%

rhum
Real number (ℝ≥0)

Distinct69
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.54593741
Minimum24
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2022-04-10T13:38:06.774783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile58
Q173
median82
Q390
95-th percentile97
Maximum100
Range76
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.91872934
Coefficient of variation (CV)0.1479743079
Kurtosis0.2126581319
Mean80.54593741
Median Absolute Deviation (MAD)8
Skewness-0.7111188468
Sum561083
Variance142.0561091
MonotonicityNot monotonic
2022-04-10T13:38:07.047311image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87256
 
3.7%
88255
 
3.7%
82251
 
3.6%
84238
 
3.4%
89232
 
3.3%
79230
 
3.3%
85223
 
3.2%
86222
 
3.2%
83222
 
3.2%
91222
 
3.2%
Other values (59)4615
66.3%
ValueCountFrequency (%)
241
 
< 0.1%
311
 
< 0.1%
321
 
< 0.1%
331
 
< 0.1%
361
 
< 0.1%
371
 
< 0.1%
381
 
< 0.1%
392
< 0.1%
404
0.1%
413
< 0.1%
ValueCountFrequency (%)
100100
1.4%
9963
 
0.9%
9888
 
1.3%
97136
2.0%
96148
2.1%
95200
2.9%
94188
2.7%
93208
3.0%
92200
2.9%
91222
3.2%

wdsp
Real number (ℝ≥0)

Distinct33
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.811369509
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2022-04-10T13:38:07.646469image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median8
Q311
95-th percentile17
Maximum35
Range34
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.38365003
Coefficient of variation (CV)0.4974992849
Kurtosis1.645777081
Mean8.811369509
Median Absolute Deviation (MAD)3
Skewness1.002281537
Sum61380
Variance19.21638759
MonotonicityNot monotonic
2022-04-10T13:38:07.862835image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
7722
10.4%
6702
10.1%
8660
9.5%
5618
8.9%
9573
 
8.2%
10560
 
8.0%
4508
 
7.3%
11464
 
6.7%
12346
 
5.0%
3340
 
4.9%
Other values (23)1473
21.1%
ValueCountFrequency (%)
131
 
0.4%
2163
 
2.3%
3340
4.9%
4508
7.3%
5618
8.9%
6702
10.1%
7722
10.4%
8660
9.5%
9573
8.2%
10560
8.0%
ValueCountFrequency (%)
352
 
< 0.1%
331
 
< 0.1%
311
 
< 0.1%
305
0.1%
294
0.1%
284
0.1%
274
0.1%
263
 
< 0.1%
256
0.1%
249
0.1%

count
Real number (ℝ≥0)

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.754378409
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.5 KiB
2022-04-10T13:38:08.070800image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile11
Maximum26
Range25
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.442080321
Coefficient of variation (CV)0.7239811442
Kurtosis1.566956878
Mean4.754378409
Median Absolute Deviation (MAD)2
Skewness1.177331745
Sum33119
Variance11.84791694
MonotonicityNot monotonic
2022-04-10T13:38:08.272507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
11229
17.6%
21000
14.4%
3898
12.9%
4775
11.1%
5645
9.3%
6597
8.6%
7471
 
6.8%
8368
 
5.3%
9274
 
3.9%
10208
 
3.0%
Other values (14)501
7.2%
ValueCountFrequency (%)
11229
17.6%
21000
14.4%
3898
12.9%
4775
11.1%
5645
9.3%
6597
8.6%
7471
 
6.8%
8368
 
5.3%
9274
 
3.9%
10208
 
3.0%
ValueCountFrequency (%)
261
 
< 0.1%
242
 
< 0.1%
231
 
< 0.1%
211
 
< 0.1%
205
 
0.1%
198
 
0.1%
188
 
0.1%
1716
0.2%
1619
0.3%
1532
0.5%

Interactions

2022-04-10T13:37:58.044236image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:43.323374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:45.857293image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:47.306332image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:49.092762image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:51.331435image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:52.806170image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:54.362822image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:56.133178image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:58.305658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:43.576391image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:46.066138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:47.461464image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:49.275610image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:51.480124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:52.959206image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:54.540970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:56.307488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:58.540346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:43.777532image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:46.226499image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:47.660269image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:49.456957image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:51.633276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:53.157393image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:54.715317image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:56.479320image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:58.762175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:43.952070image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:46.387784image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:47.813109image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:49.671793image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:51.784043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:53.330363image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:54.885781image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:56.652701image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:59.000021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:44.131862image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:46.537793image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:47.966491image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:49.901447image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:51.991771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:53.491102image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:55.101083image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:56.822816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:59.193417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:44.323706image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:46.680157image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:48.120810image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:50.209401image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:52.158256image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:53.640961image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:55.265205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:56.989190image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:59.629205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:45.396437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:46.830737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:48.536895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:50.555514image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:52.321872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:53.810926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:55.529895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:57.172470image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:38:00.499145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:45.556206image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:46.988327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:48.765487image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:50.755879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:52.489060image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:53.981651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:55.760546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:57.351728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:38:00.753615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:45.705056image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:47.157972image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:48.934230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:50.944191image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:52.657516image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:54.154279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:55.941602image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T13:37:57.525689image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-04-10T13:38:08.482070image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-10T13:38:08.736127image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-10T13:38:08.990969image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-10T13:38:09.225159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-10T13:38:09.446821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-10T13:38:01.242732image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-10T13:38:01.804245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

rental_daterental_hourrental_dayrental_monthrental_yearholidaydayofweek_ndayofweekworking_dayseasonpeaktimesofdayraintemprhumwdspcount
02021-03-012132021False0MondayTrueWinterFalseNight0.0-1.29841
12021-03-017132021False0MondayTrueWinterTrueMorning0.02.110043
22021-03-018132021False0MondayTrueWinterTrueMorning0.05.19851
32021-03-019132021False0MondayTrueWinterTrueMorning0.05.79854
42021-03-0110132021False0MondayTrueWinterTrueMorning0.06.79464
52021-03-0111132021False0MondayTrueWinterFalseMorning0.07.49184
62021-03-0112132021False0MondayTrueWinterFalseAfternoon0.06.98888
72021-03-0113132021False0MondayTrueWinterFalseAfternoon0.09.384811
82021-03-0114132021False0MondayTrueWinterFalseAfternoon0.09.380911
92021-03-0115132021False0MondayTrueWinterTrueAfternoon0.08.3791110

Last rows

rental_daterental_hourrental_dayrental_monthrental_yearholidaydayofweek_ndayofweekworking_dayseasonpeaktimesofdayraintemprhumwdspcount
69562022-02-27132722022False6SundayFalseWinterFalseAfternoon0.08.678158
69572022-02-27142722022False6SundayFalseWinterFalseAfternoon0.08.9801710
69582022-02-27152722022False6SundayFalseWinterFalseAfternoon0.08.684164
69592022-02-27162722022False6SundayFalseWinterFalseAfternoon0.08.786173
69602022-02-27172722022False6SundayFalseWinterFalseAfternoon0.08.589168
69612022-02-27182722022False6SundayFalseWinterFalseEvening0.08.770104
69622022-02-27192722022False6SundayFalseWinterFalseEvening0.08.07292
69632022-02-27202722022False6SundayFalseWinterFalseEvening0.08.666141
69642022-02-27212722022False6SundayFalseWinterFalseEvening0.09.068112
69652022-02-27222722022False6SundayFalseWinterFalseEvening0.28.674102